越聪明的AI，越危险？
我们总以为AI越强越安全，但这份对23个前沿大模型、8万多个有害输出的分析发现：模型越聪明，它产生的有害内容不仅更多，种类也更丰富。研究者把AI的“犯错”当成研究对象，像分析语言一样分析它的危险输出，结果发现这些模型表面安全，底下却藏着越来越危险的知识。这不是你明天能用上的工具，但它提醒我们：AI的能力和风险是同步增长的。
📄 原文摘要(英文)
Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of frontier-LLM misbehavior are difficult to obtain. To address this gap, we introduce HarmProfile, a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and model families, and defines the resulting harmful-output distribution as a model-level risk profile. The premise is that, just as linguistic behavior can be characterized from an utterance corpus, model risk can be characterized from the content, severity, and variation of its safety failures. HarmProfile contains over 80,000 validated artifacts from 23 frontier LLMs across 13 model families, organized into 15 harm categories and 57 subcategories. Using this corpus, we find that frontier LLMs reliably produce harmful content at scale, yet exhibit distinct risk profiles; both harmfulness and diversity grow with model capability, suggesting that frontier LLMs may appear safe yet harbor increasingly dangerous knowledge beneath the alignment surface. Our source code is available at https://github.com/fresh-ma/HarmProfile .